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1 files changed, 46 insertions, 32 deletions

diff --git a/loael.tex b/loael.tex
index f9ab237..19b9895 100644
--- a/loael.tex
+++ b/loael.tex
@@ -100,14 +100,15 @@
 \maketitle
 \begin{abstract}
 This study compares the accuracy of (Q)SAR/read-across predictions with
-the experimental variability of chronic LOAEL values from \emph{in vivo}
-experiments. We could demonstrate that predictions of the \texttt{lazar}
-algrorithm within the applicability domain of the training data have the
-same variability as the experimental training data. Predictions with a
-lower similarity threshold (i.e.~a larger distance from the
-applicability domain) are also significantly better than random
-guessing, but the errors to be expected are higher and a manual
-inspection of prediction results is highly recommended.
+the experimental variability of chronic lowest-observed-adverse-effect
+levels (LOAELs) from \emph{in vivo} experiments. We could demonstrate
+that predictions of the lazy structure-activity relationships
+(\texttt{lazar}) algorithm within the applicability domain of the
+training data have the same variability as the experimental training
+data. Predictions with a lower similarity threshold (i.e.~a larger
+distance from the applicability domain) are also significantly better
+than random guessing, but the errors to be expected are higher and a
+manual inspection of prediction results is highly recommended.
 \end{abstract}
 
 \textsuperscript{1} in silico toxicology gmbh, Basel,
@@ -166,13 +167,16 @@ methods that lead to impressive validation results, but also to
 overfitted models with little practical relevance.
 
 In the present study, automatic read-across like models were built to
-generate quantitative predictions of long-term toxicity. Two databases
-compiling chronic oral rat Lowest Adverse Effect Levels (LOAEL) as
-endpoint were used. An early review of the databases revealed that many
-chemicals had at least two independent studies/LOAELs. These studies
-were exploited to generate information on the reproducibility of chronic
-animal studies and were used to evaluate prediction performance of the
-models in the context of experimental variability.
+generate quantitative predictions of long-term toxicity. The aim of the
+work was not to predict the nature of the toxicological effects of
+chemicals, but to obtain quantitative values which could be compared to
+exposure. Two databases compiling chronic oral rat Lowest Adverse Effect
+Levels (LOAEL) as endpoint were used. An early review of the databases
+revealed that many chemicals had at least two independent
+studies/LOAELs. These studies were exploited to generate information on
+the reproducibility of chronic animal studies and were used to evaluate
+prediction performance of the models in the context of experimental
+variability.
 
 An important limitation often raised for computational toxicology is the
 lack of transparency on published models and consequently on the
@@ -334,8 +338,6 @@ structures and do not rely on predefined lists of fragments (such as
 OpenBabel FP3, FP4 or MACCs fingerprints or lists of
 toxocophores/toxicophobes). This has the advantage that they may capture
 substructures of toxicological relevance that are not included in other
-fingerprints. Unpublished experiments have shown that predictions with
-MolPrint2D fingerprints are indeed more accurate than other OpenBabel
 fingerprints.
 
 From MolPrint2D fingerprints we can construct a feature vector with all
@@ -367,6 +369,10 @@ absence of closely related neighbors, we follow a tiered approach:
   similarity threshold of 0.2 and the prediction is flagged with a
   warning that it might be out of the applicability domain of the
   training data.
+\item
+  Similarity thresholds of 0.5 and 0.2 are the default values chosen by
+  the software developers and remained unchanged during the course of
+  these experiments.
 \end{itemize}
 
 Compounds with the same structure as the query structure are
@@ -393,11 +399,12 @@ resampling.
 
 Finally the local RF model is applied to
 \href{https://github.com/opentox/lazar/blob/loael-paper.submission/lib/model.rb\#L194-L272}{predict
-the activity} of the query compound. The RMSE of bootstrapped local
-model predictions is used to construct 95\% prediction intervals at
-1.96*RMSE. The width of the prediction interval indicates the expected
-prediction accuracy. The ``true'' value of a prediction should be with
-95\% probability within the prediction interval.
+the activity} of the query compound. The root-mean-square error (RMSE)
+of bootstrapped local model predictions is used to construct 95\%
+prediction intervals at 1.96*RMSE. The width of the prediction interval
+indicates the expected prediction accuracy. The ``true'' value of a
+prediction should be with 95\% probability within the prediction
+interval.
 
 If RF modelling or prediction fails, the program resorts to using the
 \href{https://github.com/opentox/lazar/blob/loael-paper.submission/lib/regression.rb\#L6-L16}{weighted
@@ -724,15 +731,15 @@ In order to establish the level of safety concern of food chemicals
 toxicologically not characterized, a methodology mimicking the process
 of chemical risk assessment, and supported by computational toxicology,
 was proposed (Schilter et al. 2014). It is based on the calculation of
-margins of exposure (MoE) between predicted values of toxicity and
-exposure estimates. The level of safety concern of a chemical is then
-determined by the size of the MoE and its suitability to cover the
-uncertainties of the assessment. To be applicable, such an approach
-requires quantitative predictions of toxicological endpoints relevant
-for risk assessment. The present work focuses on the prediction of
-chronic toxicity, a major and often pivotal endpoint of toxicological
-databases used for hazard identification and characterization of food
-chemicals.
+margins of exposure (MoE) that is the ratio between the predicted
+chronic toxicity value (LOAEL) and exposure estimate. The level of
+safety concern of a chemical is then determined by the size of the MoE
+and its suitability to cover the uncertainties of the assessment. To be
+applicable, such an approach requires quantitative predictions of
+toxicological endpoints relevant for risk assessment. The present work
+focuses on the prediction of chronic toxicity, a major and often pivotal
+endpoint of toxicological databases used for hazard identification and
+characterization of food chemicals.
 
 In a previous study, automated read-across like models for predicting
 carcinogenic potency were developed. In these models, substances in the
@@ -845,7 +852,14 @@ variability as the experimental training data. In such cases
 experimental investigations can be substituted with \emph{in silico}
 predictions. Predictions with a lower similarity threshold can still
 give usable results, but the errors to be expected are higher and a
-manual inspection of prediction results is highly recommended.
+manual inspection of prediction results is highly recommended. Anyway,
+our suggested workflow includes always the visual inspection of the
+chemical structures of the neighbors selected by the model. Indeed it
+will strength the prediction confidence (if the input structure looks
+very similar to the neighbors selected to build the model) or it can
+drive to the conclusion to use read-across with the most similar
+compound of the database (in case not enough similar compounds to build
+the model are present in the database).
 
 \section*{References}\label{references}
 \addcontentsline{toc}{section}{References}